The C-KBQA systems described above use the modular architecture of Figure
REF , where each module (e.g., semantic parser, state tracker, dialog policy, response generator) needs to be developed or trained individually.
Recently, there is a growing interest in developing unitry systems which directly generate natural language responses (e.g., answers) given a user input using a Grounded Text Generation (GTG) model.
Unlike a regular neural language model used for natural language generation (such as GPT-2), GTG is a stateful decision making model.
Specifically, GTG is a hybrid model which uses a large-scale Transformer neural network [1]} as its backbone, combined with symbol-manipulation modules for knowledge base inference, to generate responses grounded in dialog state and real-world knowledge for completing tasks (e.g., C-KBQA).
